Description:FIELD OF THE INVENTION
The present invention relates to the field of artificial intelligence and secure computing systems. More particularly, it pertains to a system and method for dynamically managing access control in artificial intelligence environments based on real-time user context, contextual signals, and organizational policies.
BACKGROUND OF THE INVENTION
As artificial intelligence technologies, including large language models (LLMs), are increasingly integrated into business and personal workflows, users are interacting with artificial intelligence systems for a wide range of tasks such as content generation, analysis, planning, and decision-making. These systems are often embedded in automated or semi-automated workflows, also known as agentic workflows, which operate with limited human intervention.
However, as the use of artificial intelligence systems expands, so do concerns related to data sensitivity, security, model misuse, and the need to ensure compliance with organizational policies. Most existing systems rely on static access controls, meaning every user or session is treated the same regardless of the task type, device being used, data sensitivity, or the user's role and context. These systems do not adapt in real time to different operational scenarios, nor do they allow dynamic switching between artificial intelligence capabilities based on intent, risk, or urgency.
This lack of adaptability can result in security vulnerabilities, inefficient model usage, and poor user experience especially in environments where the same artificial intelligence platform is used for tasks of varying sensitivity, urgency, or operational risk.
Therefore, there is a need for a smarter and more flexible access control system that can understand the context of the user and environment, adjust permissions dynamically, and control access to artificial intelligence models, tools, memory, and automation levels accordingly.
Prior Art:
For instance, US20230237126A1 discloses systems and methods for managing access to artificial intelligence services based on contextual attributes such as user identity, device condition, and session metadata. While the system introduces contextual awareness, it primarily relies on static policy rules and does not construct dynamic, runtime access profiles that synthesize multi-source signals in real time. It also lacks a graph-based environment representation or adaptive artificial intelligence capability control such as switching between manual, semi-automated, or fully automated modes based on task risk, user behavior, or model confidence.
US20240143722A1 describes access governance for artificial intelligence platforms using federated identity, token-based permissions, and user-level authorization rules. Although it supports distributed access control, the system is focused on identity management and does not orchestrate access to artificial intelligence model types, operational modes, or toolchains based on real-time user intent or data classification. It further lacks a semantic control layer to influence artificial intelligence prompt behavior or downstream execution settings using declarative markers (e.g., #secure, #auditmode), nor does it incorporate a risk-mapping mechanism that tunes artificial intelligence behavior based on task sensitivity or organizational trust levels.
US9602505B1 outlines methods for enforcing data access policies in computing environments using policy definition files and user attributes. While effective for general access control, the system is not tailored for artificial intelligence-first architectures and does not account for context-aware orchestration of artificial intelligence tools, memory scopes, or operational boundaries. It does not support dynamic profile generation, composable control over plugins or models, or the ability to regulate artificial intelligence access per session based on evolving user-environment signals or intent-risk gradients.
Although these prior art systems enable various forms of access restriction or policy enforcement, they do not present a comprehensive framework for real-time, adaptive artificial intelligence access control. Current approaches lack key capabilities such as intent-driven session profiling based on contextual fingerprints, automated mode selection across manual, semi-automated, and automated artificial intelligence workflows, prompt-level semantic overlays to guide model behavior, continuous risk scoring for tuning artificial intelligence scope, memory, and trust, and dynamic enforcement of organizational governance in artificial intelligence session orchestration.
DEFINITIONS
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but is not limited to a system with a user, input and output devices, processing unit, plurality of mobile devices, a mobile device-based application to identify dependencies and relationships between diverse businesses, a visualization platform, and output; and is extended to computing systems like mobile, laptops, computers, PCs, etc.
The expression “input unit” used hereinafter in this specification refers to, but is not limited to, mobile, laptops, computers, PCs, keyboards, mouse, pen drives or drives.
The expression “output unit” used hereinafter in this specification refers to, but is not limited to, an onboard output device, a user interface (UI), a display kit, a local display, a screen, a dashboard, or a visualization platform enabling the user to visualize, observe or analyse any data or scores provided by the system.
The expression “processing unit” refers to, but is not limited to, a processor of at least one computing device that optimizes the system.
The expression “large language model (LLM)” used hereinafter in this specification refers to a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
The expression “adaptive profile”, as used hereinafter in this specification, refers to a dynamic, session-specific configuration that defines the access permissions, artificial intelligence model selection, memory scope, prompt handling, tool availability, and automation level for a given user interaction, based on real-time contextual signals such as user role, device status, task intent, data sensitivity, and organizational policies.
The expression “profile orchestration engine (POE)”, as used hereinafter in this specification, refers to the system module responsible for synthesizing multiple contextual inputs including user identity, device metadata, network trust signals, time-of-day, and policy constraints into a runtime adaptive profile that governs artificial intelligence system behavior during a session.
The expression “context mesh resolver (CMR)”, as used hereinafter in this specification, refers to the subsystem that constructs a real-time, graph-based representation of the operational environment, wherein nodes represent users, tasks, data types, and compute resources, and edges denote relationships such as trust, urgency, and data flow, collectively forming a contextual fingerprint used for profile generation and access decisions.
The expression “adaptive mode selector (AMS)”, as used hereinafter in this specification, refers to the module that determines the appropriate level of artificial intelligence automation manual, semi-automated, or automated for a given session, based on inputs such as contextual fingerprint, model confidence scores, task urgency, and operational risk level.
The expression “semantic hashtag overlay (SHO)”, as used hereinafter in this specification, refers to a control layer that applies declarative, tag-based modifiers (e.g., #secure, #nopii, #localmodel) to user prompts or session metadata, which in turn influence artificial intelligence model behavior, memory access, redaction strategies, logging policies, and downstream orchestration decisions.
The expression “intent-risk gradient mapper (IRGM)”, as used hereinafter in this specification, refers to the component that maps detected or declared user intent to a continuous operational risk score, which is subsequently used to adjust session parameters such as token limits, model trust level, memory scope, and human review triggers.
The expression “auto-sanitizer”, as used hereinafter in this specification, refers to the component responsible for intercepting user inputs prior to artificial intelligence processing, redacting or substituting sensitive or policy-prohibited content using memory-based placeholders, thereby enforcing privacy and compliance requirements in real time.
OBJECTS OF THE INVENTION
The primary object of the present invention is to provide a system and method for dynamic access control to artificial intelligence systems through adaptive profiles that respond to real-time contextual signals.
Another object of the invention is to generate runtime profiles by combining user identity, device status, network trust, data sensitivity, task metadata, and organizational policies for secure and context-aware artificial intelligence interaction.
A further object is to enable automated switching between manual, semi-automated, and fully automated artificial intelligence modes based on task urgency, model confidence, and operational risk.
Another object is to offer fine-grained control over artificial intelligence model selection, memory scope, tool access, and prompt behavior using semantic hashtags embedded in session metadata.
Yet another object is to construct a graph-based context model using a Context Mesh Resolver to produce a real-time contextual fingerprint for profile orchestration.
An additional object is to map user intent to a continuous operational risk score to govern token limits, memory usage, authentication needs, and audit triggers.
A final object is to enforce organizational policies by translating static definitions into executable, session-level constraints across artificial intelligence workflows.
SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention provides a system and method for dynamic access control in artificial intelligence (AI) environments by generating real-time, adaptive access profiles tailored to each session. Unlike static role-based access models, the disclosed invention continuously evaluates contextual inputs including user identity, device characteristics, network trust levels, task metadata, data sensitivity, and organizational policies. These inputs are synthesized by a profile orchestration engine that generates a runtime access profile governing the permissible AI models (e.g., foundation or fine-tuned), tools, memory settings (off, limited, long-term), and automation modes (manual, semi-automated, automated) for that session.
According to an aspect of the present invention, the system and method for dynamic access control in artificial intelligence (AI) environments operate by generating adaptive, context-aware profiles in real time. When a user submits a request through the input unit such as initiating a prompt or accessing a plugin the profile orchestration engine evaluates multiple contextual signals, including user identity, role, device metadata, IP address, network trust level, task type, data sensitivity, and organizational policy constraints. In parallel, the context mesh resolver constructs a graph-based model of the operational environment, generating a unique contextual fingerprint that reflects session-specific trust, urgency, and data relationships. Based on this synthesis, the profile orchestration engine generates a runtime access profile that defines permissible AI capabilities such as model type (foundation or fine-tuned), automation mode (manual, semi-automated, or automated), tool and plugin access, memory settings, and prompt handling rules.
BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
FIG. 1 illustrates the architecture of the system for adaptive access control in artificial intelligence environments based on user and environment context.
FIG. 2 illustrates a flow diagram showing the process of context gathering, adaptive profile generation, and access enforcement.
FIG. 3 illustrates a sequence diagram showing the interactions among system components during runtime profile application.
FIG. 4 illustrates the decision logic for semantic hashtag processing and intent-risk-based session control.
DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
The present invention provides a system and method for dynamic access control in artificial intelligence (AI) environments by generating real-time, adaptive access profiles tailored to each session. Unlike static role-based access models, the disclosed invention continuously evaluates contextual inputs including user identity, device characteristics, network trust levels, task metadata, data sensitivity, and organizational policies. These inputs are synthesized by a profile orchestration engine that generates a runtime access profile governing the permissible AI models (e.g., foundation or fine-tuned), tools, memory settings (off, limited, long-term), and automation modes (manual, semi-automated, automated) for that session.
According to the embodiment of the present invention, a "session" refers to a discrete interaction period between a user and an AI system, during which the user issues requests (e.g., prompts, plugin calls, or tool invocations), the system evaluates real-time contextual signals, and an adaptive access profile is created and enforced specifically for that interaction. The key characteristics of a "Session" are:
• Session-specific context: -The system dynamically gathers and interprets contextual metadata for that particular usage instance—such as user identity and role, device attributes (e.g., IP, OS, security posture), network trust level (e.g., VPN, ZTNA), task metadata or inferred intent, time-of-day, organizational policies.
• Session scope: The access control decisions (e.g., whether to use a foundation or fine-tuned model, memory scope, level of automation, and plugin/tool access) are scoped to that specific session. Once the session ends, the context and access profile may be discarded or logged, and future sessions re-evaluate from scratch or use learning loops to adapt.
• Runtime behavior: The session governs how the AI behaves in real time, based on components like: the Profile Orchestration Engine (POE), Context Mesh Resolver (CMR), Adaptive Mode Selector (AMS), Semantic Hashtag Overlay (SHO), etc.
• Lifecycle: A session starts when a user initiates a request (prompt, query, script execution, etc.) It ends when the response is delivered and any logging or enforcement is completed.
According to the embodiment of the present invention, the system includes an input unit, a processing unit, and an output unit. The core of the system lies in the processing unit, which consists of several key components: a profile orchestration engine, a context mesh resolver, an adaptive mode selector, a semantic hashtag overlay, an intent-risk gradient mapper, security and governance integration module comprising of policy digestor, inference logging pipelines, and auto sanitizer. The input unit captures user requests and contextual data, and the output unit delivers the AI response while logging session outcomes.
The context mesh resolver constructs a real-time, graph-based environmental model using data from the input unit, representing relationships among users, devices, tasks, and data flows. This model generates a unique contextual fingerprint that informs trust and risk posture for the current session. The profile orchestration engine uses this fingerprint along with historical baselines and structured policy definitions to configure session parameters. The adaptive mode selector then evaluates variables such as task urgency, model confidence, and prior success to determine the level of automation appropriate for the interaction. The semantic hashtag overlay introduces user- or admin-declared controls e.g., #secure, #nopii, #localmodel at the prompt level, enabling declarative influence over memory access, model behavior, redaction, and audit settings. The Semantic Hashtag Overlay (SHO) enables declarative, prompt-level control over AI system behavior using special tag-like directives, much like hashtags. The Semantic Hashtag Overlay is a control layer that applies predefined semantic tags (called hashtags) to user prompts, session metadata, or both, to influence AI behavior dynamically during a session. These hashtags are interpreted by the system to enforce constraints or modify AI behavior without requiring changes in the backend code or reconfiguration of access settings. They act as short, declarative signals embedded in the interaction.
Examples: #secure → activates enhanced logging, disables long-term memory, and routes the prompt to an internal model, #nopii → disables PII-related processing or triggers sanitization routines, #localmodel → forces use of an on-premise model instead of cloud-based ones. #auditmode → increases audit logging granularity and may route to human review, #readonly → restricts tool usage and disables automated actions.
Simultaneously, the intent-risk gradient mapper assesses the user’s purpose stated or inferred and computes a dynamic risk score to modulate runtime parameters such as token limits, plugin/tool permissions, memory scope, and authentication needs. These scores allow the system to continuously adapt to the evolving sensitivity and risk of a session. The Security and Governance Integration module comprises of Policy Digestor, Inference Logging Pipeline and Auto-Sanitizer. The policy digestor module interprets YAML/JSON-based governance rules and enforces them in real time as constraints applied directly to the session. These formats are human-readable, machine-readable, and widely used to define structured data, making them suitable for configuring and enforcing governance across systems. The Inference Logging Pipeline structures logs by combining SHO tags, IRGM scores, and CMR edges. The Auto-Sanitizer intercepts prompt inputs, redacts sensitive data, and uses memory to substitute meaning-preserving placeholders for LLM processing.
SHO tags, IRGM scores, and CMR edges are key context signals that work together to drive dynamic, secure, and adaptive AI behavior in the present system. As described earlier, the SHO tags are special control tags like #secure, #nopii, or #localmodel that users or administrators can attach to prompts to influence how the AI behaves. These tags act like instructions telling the system to enforce certain policies — for example, to keep data private, avoid storing personal information, or only use local models. They are easy to use, transparent, and powerful, allowing fine-tuned control over memory access, logging, tool usage, and model selection at the prompt level. By embedding these tags directly into the user input or session metadata, the system can quickly and reliably adapt its behavior to meet specific security, compliance, or operational needs.
IRGM Scores (Intent-Risk Gradient Mapper Scores) represent a calculated level of risk for a specific AI interaction, based on what the user wants to do (intent) and how sensitive or critical that task is. The system automatically analyzes the context and the nature of the request — for example, whether the user is writing a casual email or accessing confidential financial data — and assigns a numerical score. Higher scores indicate higher risk, prompting the system to respond with stricter controls, like limiting token usage, disabling memory, requiring human approval, or switching to more secure models. This ensures that the AI behaves differently for low-risk vs. high-risk tasks, without the user needing to explicitly ask for it.
CMR Edges (Context Mesh Resolver Edges) are the connections in a graph built by the Context Mesh Resolver to map the real-time environment of a user session. This graph includes nodes like users, devices, tasks, and data types, and edges that show relationships such as trust, urgency, or data flow between them. For example, the system might connect a trusted user on a secure device working on an urgent task to a specific model or plugin. These edges form a “contextual fingerprint” that helps the system understand how safe or sensitive the session is. This fingerprint is then used to create an adaptive access profile that decides what the user can do and what resources the AI can access.
SHO tags, IRGM scores, and CMR edges work together as a layered, real-time decision-making system that makes AI sessions secure, context-aware, and adaptive. When a user initiates a prompt, the system first examines any SHO tags (like #secure or #localmodel) to immediately apply rule-based behavior changes — such as limiting memory or choosing a local AI model. Simultaneously, the Context Mesh Resolver builds a dynamic graph of the session’s environment, using CMR edges to understand the relationships between users, tasks, data, and devices — essentially creating a contextual fingerprint. This fingerprint is passed to the Intent-Risk Gradient Mapper, which calculates an IRGM score representing how risky the session is based on the user’s intent and sensitivity of the task. The system then uses all three signals — tag directives, graph context, and risk score — to generate and enforce an adaptive access profile for that session. This ensures that every AI interaction is tuned precisely to the user’s real-time situation, balancing usability with security and compliance.

According to the embodiment of the present invention, the method for dynamically controlling access to artificial intelligence systems using user-environment-aware adaptive profiles, as illustrated in FIG. 1, comprises the following steps:
● Receiving the user request at the input unit (FIG. 1): The process begins when a user initiates a request, such as a query, prompt, or workflow interaction. This request is received by the input unit and passed to the processing unit.
● Generating the contextual fingerprint via the Context Mesh Resolver (FIG. 1): The system constructs a real-time representation of the operational environment using the context mesh resolver. This includes nodes for user identity, task type, data sensitivity, and device attributes, and edges representing trust, urgency, and data flow relationships. The result is a contextual fingerprint unique to the session.
● Synthesizing access profiles using the Profile Orchestration Engine (FIG. 1): The profile orchestration engine synthesizes inputs such as user role, device metadata, IP address, geolocation, network trust, data classification, time-of-day, and organizational policy. Based on this data and the contextual fingerprint, a runtime access profile is generated that defines artificial intelligence model access, operational mode (manual/semi-automated/automated), memory scope, tool permissions, and prompt handling rules.
● Selecting operational mode via the Adaptive Mode Selector (FIG. 2): Using signals such as task urgency, model confidence, and historical success, the adaptive mode selector dynamically selects the appropriate automation level for the session: manual, semi-automated, or automated.
● Applying semantic controls using the Semantic Hashtag Overlay (FIG. 2): The system tags the session or prompts with declarative semantic hashtags such as #secure, #nopii, or #localmodel. These hashtags influence access permissions, model usage, memory behavior, logging, and audit policies.
● Mapping user intent and risk via the Intent-Risk Gradient Mapper (FIG. 2): The user’s inferred or declared intent is mapped to a continuous risk score. This score adjusts token limits, model trust thresholds, memory access, and authentication requirements.
● Enforcing policy overlays through the Policy Digestor (FIG. 2): Organization-defined YAML/JSON policy files are translated into real-time session-level constraints. These overlays dynamically restrict or allow artificial intelligence capabilities in accordance with governance rules.
● Validating session behavior and output (FIG. 3): Session activity is continuously monitored against the adaptive profile. If actions deviate e.g., unauthorized model use or exceeds the risk thresholds the system triggers fallback responses, re-profiles the session, or limits access dynamically.
● Delivering the output via the output unit (FIG. 1): Once validated, the artificial intelligence system’s response is passed through the output unit and delivered to the user.
According to an embodiment of the present invention, the system and method for dynamic access control in artificial intelligence (AI) environments operate by generating adaptive, context-aware profiles in real time. When a user submits a request through the input unit such as initiating a prompt or accessing a plugin the profile orchestration engine evaluates multiple contextual signals, including user identity, role, device metadata, IP address, network trust level, task type, data sensitivity, and organizational policy constraints. In parallel, the context mesh resolver constructs a graph-based model of the operational environment, generating a unique contextual fingerprint that reflects session-specific trust, urgency, and data relationships. Based on this synthesis, the profile orchestration engine generates a runtime access profile that defines permissible AI capabilities such as model type (foundation or fine-tuned), automation mode (manual, semi-automated, or automated), tool and plugin access, memory settings, and prompt handling rules.
The runtime access profile is further refined using the adaptive mode selector, which determines the appropriate level of automation based on task complexity, model confidence, and operational risk. Declarative control is enabled through the semantic hashtag overlay, which allows users or administrators to apply session-level tags such as #secure, #nopii, or #localmodel. These tags influence runtime behavior related to memory access, redaction, audit logging, and model invocation. Additionally, the intent-risk gradient mapper analyzes declared or inferred user intent and calculates a continuous risk score. This score influences token limits, model trust thresholds, and memory scope on a session-specific basis. The integration of these components ensures that the AI system responds securely and intelligently, adapting in real time to the needs and constraints of each session.
Enforcement of the access profile is handled dynamically by the policy digestor, which interprets structured governance files (e.g., YAML or JSON) and applies them as runtime overlays. During the session, if any deviation from the approved profile is detected such as invocation of an unauthorized model, unexpected escalation in automation, or policy violation the system enforces corrective actions. These may include profile regeneration, fallback to a lower automation mode, or access restriction. The redaction and audit pipeline ensures that sensitive content is sanitized, usage is logged, and every action is compliant with the organization’s policies and the session’s contextual fingerprint. This enforcement architecture guarantees that access is not only dynamically tailored but also verifiably compliant.
All key inputs and decisions including contextual signals, generated access profiles, semantic hashtag effects, risk scores, and enforcement outcomes are captured by the feedback and learning loop. This data is continuously logged and analyzed to refine future profile orchestration strategies, improve the accuracy of intent-risk mapping, and evolve policy enforcement logic. Over time, this learning mechanism allows the system to adapt to emerging usage patterns, shifting risk conditions, and updated governance requirements. The result is a robust, context-sensitive, and policy-aware framework for secure AI access control across diverse user environments.

According to an embodiment of the present invention, the adaptive access control system performs a structured sequence of operations on incoming user requests to ensure that each artificial intelligence session is securely configured and contextually appropriate. The orchestration process includes:
1. Contextual Analysis: The system begins by collecting real-time contextual signals from the user’s session, including user identity, role, device metadata, IP address, network trust level, task metadata, and data sensitivity. These signals are used by the profile orchestration engine to initiate access control.
2. Context Mesh Resolution: The context mesh resolver constructs a graph-based representation of the operational environment, where nodes represent users, devices, and tasks, and edges represent trust, urgency, and risk relationships. This results in a unique contextual fingerprint for the session.
3. Access Profile Generation: Using the contextual fingerprint and organizational policies, the profile orchestration engine generates a runtime adaptive profile. This profile defines permissions related to artificial intelligence model access (e.g., foundation or fine-tuned), automation mode (manual, semi-automated, or automated), memory scope, tool/plugin access, and prompt handling rules.
4. Mode Selection: The adaptive mode selector determines the appropriate level of artificial intelligence automation based on task complexity, urgency, and operational risk.
5. Semantic Control Overlay: The semantic hashtag overlay allows declarative tags such as #secure, #nopii, or #localmodel to modify model behavior, memory access, redaction settings, or audit logging within the session.
6. Intent-Risk Mapping: The intent-risk gradient mapper evaluates the user's stated or inferred intent and calculates a continuous operational risk score. This score influences session controls such as token limits, authentication level, and memory access.
7. Policy Enforcement: The policy digestor interprets organization-defined governance rules and converts them into runtime constraints that dynamically apply to each session, ensuring policy compliance in real time.
8. Validation and Enforcement: The system continuously monitors session behavior to ensure that it remains within the approved boundaries of the adaptive profile. If deviations are detected such as unauthorized model use or elevated risk the system enforces fallback actions such as access restriction, session downgrade, or profile regeneration.
9. Logging and Learning: All session parameters, decisions, and enforcement actions are recorded by the feedback and learning loop. This data is used to refine future profile orchestration strategies and adapt to evolving usage patterns, user behavior, and policy updates.
10. This modular, intelligent, and policy-aware orchestration framework enables secure, dynamic, and context-sensitive access to artificial intelligence systems across a wide range of operational scenarios.
Advantages:
The present invention offers several advantages that make artificial intelligence systems more secure, flexible, and intelligent. It generates user-specific access profiles in real time by combining information such as the user’s identity, the device being used, the trust level of the network, the sensitivity of the data, the nature of the task, and applicable organizational policies. This allows the system to determine, for each session, which artificial intelligence tools, models, memory settings, and automation levels the user can access.
The system can automatically switch between manual, semi-automated, and fully automated modes depending on task urgency, risk level, or confidence in the artificial intelligence response. It also supports simple control mechanisms through semantic hashtags (e.g., #secure, #nopii), allowing users or administrators to influence artificial intelligence behavior in a transparent and efficient manner. Additionally, it uses user intent to measure task risk and dynamically adjust session parameters to reduce exposure or misuse.
Organizational policies are enforced automatically during each interaction, ensuring compliance across workflows. The modular architecture makes the system easy to integrate and suitable for use in enterprise environments, especially those handling sensitive or regulated data.
, C , Claims:We claim,
1. A system and method for dynamic artificial intelligence access control by generating adaptive profiles in real time
characterized in that
the system comprises of input unit, a processing unit, and an output unit and the processing unit comprises of a profile orchestration engine, a context mesh resolver, an adaptive mode selector, a semantic hashtag overlay, an intent-risk gradient mapper, and governance integration module comprising of policy digestor, inference logging pipelines, and auto sanitizer;
the method for dynamic artificial intelligence access control comprises the following steps:
• receiving the user request at the input unit and passed to the processing unit;
• generating the contextual fingerprint that includes nodes for user identity, task type, data sensitivity, and device attributes, and edges representing trust, urgency, and data flow relationships via the Context Mesh Resolver;
• synthesizing access profiles using the Profile Orchestration Engine based on inputs including user role, device metadata, IP address, geolocation, network trust, data classification, time-of-day, and organizational policy and the contextual fingerprint;
• selecting operational mode as manual, semi-automated, or automated via the Adaptive Mode Selector using signals such as task urgency, model confidence, and historical success;
• applying semantic controls for access permissions, model usage, memory behavior, logging, and audit policies using the Semantic Hashtag Overlay;
• mapping user intent and risk to a continuous risk score via the Intent-Risk Gradient Mapper such that the score adjusts token limits, model trust thresholds, memory access, and authentication requirements.
• enforcing policy overlays through the Policy Digestor;
• validating session behavior and output against the adaptive profile;
• delivering the output via the output unit after validation.

2. The system and method as claimed in claim 1, wherein the input unit receives a user request to interact with an artificial intelligence system such as asking a question, running a script, or accessing a tool and the profile orchestration engine collects contextual data, including user identity, device characteristics, network trust level, data sensitivity, task metadata, and organizational policies, based on which, it creates a real-time access profile that defines what the user can access such as specific models, tools, memory scopes, or automation levels.

3. The system and method as claimed in claim 1, wherein the profile orchestration engine dynamically generates a runtime access profile for each user session by synthesizing multiple contextual signals and these signals include user identity and role, device characteristics, IP address, geolocation, network trust signals, task metadata or inferred task intent, data classification level, time-of-day, and organizational policy schedules.

4. The system and method as claimed in claim 1, wherein the resulting access profile determines the appropriate configuration for that session, including the artificial intelligence mode (manual, semi-automated, or automated), model type (e.g., on-premise or cloud, foundation or fine-tuned), memory settings (off, limited, or long-term), tool and plugin permissions (allowlist or blocklist), and prompt handling strategies such as redaction or augmentation.

5. The system and method as claimed in claim 1, wherein the semantic hashtag overlay introduces user- or admin-declared controls at the prompt level, enabling declarative influence over memory access, model behavior, redaction, and audit settings using special tag-like directives, much like hashtags such that the Semantic Hashtag Overlay is a control layer that applies predefined semantic tags or hashtags to user prompts, session metadata, or both, to influence AI behavior dynamically during a session that are interpreted by the system to enforce constraints or modify AI behavior without requiring changes in the backend code or reconfiguration of access settings.

6. The system and method as claimed in claim 1, wherein the validation of access decisions is achieved through coordinated interaction among the profile orchestration engine, semantic hashtag overlay, intent-risk gradient mapper, and policy digestor.

7. The system and method as claimed in claim 1, wherein for every session, the profile orchestration engine generates an adaptive access profile using a weighted evaluation of contextual signals, including user identity, role, device status, network trust level, task metadata, data classification, and organizational policy.

8. The system and method as claimed in claim 1, wherein the policy digestor module interprets YAML/JSON-based governance rules and enforces them in real time as constraints applied directly to the session, the Inference Logging Pipeline structures logs by combining SHO tags, IRGM scores, and CMR edges and the Auto-Sanitizer intercepts prompt inputs, redacts sensitive data, and uses memory to substitute meaning-preserving placeholders for LLM processing.

9. The system and method as claimed in claim 1, wherein SHO tags, IRGM scores, and CMR edges are key context signals that work together to drive dynamic, secure, and adaptive AI behavior in the system. a layered, real-time decision-making system that makes AI sessions secure, context-aware, and adaptive such that when a user initiates a prompt, the system first examines any SHO tags to immediately apply rule-based behavior changes such as limiting memory or choosing a local AI model, and simultaneously, the Context Mesh Resolver builds a dynamic graph of the session’s environment, using CMR edges to understand the relationships between users, tasks, data, and devices by creating a contextual fingerprint, that is passed to the Intent-Risk Gradient Mapper, which calculates an IRGM score representing how risky the session is based on the user’s intent and sensitivity of the task such that the system then uses all three signals tag directives, graph context, and risk score to generate and enforce an adaptive access profile for that session.

10. The system and method as claimed in claim 1, wherein IRGM Scores, Intent-Risk Gradient Mapper Scores represent a calculated level of risk for a specific AI interaction, based on what the user wants to do and how sensitive or critical that task is such that higher scores indicate higher risk, prompting the system to respond with stricter controls, like limiting token usage, disabling memory, requiring human approval, or switching to more secure models and CMR Edges -Context Mesh Resolver Edges are the connections in a graph built by the Context Mesh Resolver to map the real-time environment of a user session that includes nodes like users, devices, tasks, and data types, and edges that show relationships such as trust, urgency, or data flow between them.